This chapter surveys how Artificial Intelligence and Deep Learning will have a significant effect on the healthcare domain in the future, particularly in the field of medical virology. As the healthcare area faces an unprecedented increase in data volume and complexity, artificial intelligence (AI) provides a track to transform patient care through effective diagnostic accuracy or systematic treatment planning and prognostic patient monitoring. By applying advanced AI and Deep Learning Models, clinical professionals can study complicated datasets and recognize and uncover nuanced illness patterns and risk factors that could otherwise remain unnoticed. This technological accuracy facilitates cautious and highly individualized care in the treatment of viral diseases, where early diagnosis and customized therapies can significantly improve the outcomes of the treatment. Moreover, this chapter deals with the advantages and difficulties of integrating AI and Deep Learning into healthcare, from making disease classification much more efficient and reducing diagnostic error rates to managing and handling ethical and resource-related issues. Healthcare becomes more accessible and scalable due to AI’s ability to automate and reduce complex medical procedures by reducing time delay in diagnosis. We deal with various real-life situations, applications, and different case studies to show how AI-driven solutions are enhancing disease diagnosis, improving patient care, and enhancing the healthcare system to adjust to changing demands. This chapter highlights how AI has the ability to completely transform healthcare globally by integrating the expertise of medical professionals with AI’s analytical abilities. It achieves this by providing a proactive, patient-oriented approach that increases the clinical results and sets a new point of reference for clarity in medicine. This chapter also talks about the challenges that currently exist in implementing these technologies, which include concerns with data privacy, moral dilemmas, and the processing power required to support large-scale AI applications, and how to overcome these kinds of challenges.